Why you should Enroll in a Data Science Course Today

Microsoft recently launched a data science course following the common verdict from tech companies of the world: The world is falling short of AI engineers. This 10 module data science course from under the Microsoft belt is just another pointer that data science engineers will have more opportunities to tap into in the coming future.

Microsoft is the not the only one in the league. Google had also launched ‘Learn with Google AI‘ that focused on Google Cloud TensorFlow, the AI framework of the company. The only problem with this course was because it was more applicable for lone developers. Microsoft’s data science course, on the other hand, comes with the tagline that it is designed to improve your resume.

Benefits of enrolling for a data science course

Demand for data science engineers are touching the skyline, what with IBM predicting that employment rate for data scientists will reach 28% in coming one year’s time. Businesses across the world are actively investing in big data and related technologies to make better business decisions and investments. This makes way for more data science job scopes.

Increase in job opportunities is the sole catalyzing factor that interested people are enrolling into various data science courses every day. Employees are now aware that advanced knowledge in data science can give them the much-needed edge to solve business issues. The best part is, irrespective of their position or background, a person willing to enroll in a data science course and complete it dedicatedly will stay ahead of the curve professionally.

A data science course is usually designed specifically to train professionals and data science enthusiasts to handle complex data. They become competent in making valuable predictions, build predictive modeling and trigger data-driven automation without having to dig into complex coding algorithms from scratch.

When someone has an in-depth understanding of the data science principles, that person is in a better position by default to put data tools into use. So, if you are still wondering whether you should enroll for a data science course or not- doubts cleared. You should. Doing a data science course can give your resume the much-needed boost.

Which data science course to start with?

There are many data science courses that are free, and then there are those that are heavily priced. ofcourse, free data science courses are great, but when you go for some excellent paid courses, it offers you more credibility. Let’s get honest here: nothing comes for free entirely.

To do a data science course, you won’t need to scout around. You can start with one these four data science courses from AnalytixLabs.

1. Data Science using SAS and R

Data science using SAS and R course will help you go over the basic statistical concepts and foray into advanced analytics and predictive modeling techniques with SAS and R. It also includes modules on machine learning using R.

2. Data Science using R

Enroll for Data Science using R course to get hands-on training on data analytics with R, which is helmed as the golden boy of data science.

3. Data Science using Python

Data science using Python will train you to handle the complex challenges of big data analytics. Learn how to handle data and data manipulation, clubbed with visualization and predictive analytics.

4. Business Analytics 360

One of the most comprehensive courses, Business Analytics 360 is designed for beginners who want to transit from Excel, VBA, and Tableau to more advanced matters like R, SAS, data science, machine learning and business analytics. This is one course that will help you get ready for data science jobs and put your business analytics skills to test.

So, let’s get going already.

There are multiple benefits of doing a data science course, the most important being the high demand for data experts. If you are looking to make a career with big data, then now is the right time to get started with a data science course that suits you. To be frank, you can just do all of these, one after the other. That way, you’ll leave no stones unturned to get big-data-ready!